Humans Disagree With the IoU for Measuring Object Detector Localization Error

Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan, Oana Inel, Jan van Gemert

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on localization quality. To evaluate this, we conduct a survey with more than 70 participants. Results show that for localization errors with the exact same IoU score, humans might not consider that these errors are equal, and express a preference. Our work is the first to evaluate IoU with humans and makes it clear that relying on IoU scores alone to evaluate localization errors might not be sufficient.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE International Conference on Image Processing (ICIP)
Place of PublicationPiscataway
PublisherIEEE
Pages1261-1265
Number of pages5
ISBN (Electronic)978-1-6654-9620-9
ISBN (Print)978-1-6654-9621-6
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Image Processing (ICIP) - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022

Conference

Conference2022 IEEE International Conference on Image Processing (ICIP)
Country/TerritoryFrance
CityBordeaux
Period16/10/2219/10/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • object detection
  • IoU
  • human preference

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